Overview

Dataset statistics

Number of variables22
Number of observations25437
Missing cells0
Missing cells (%)0.0%
Total size in memory3.6 MiB
Average record size in memory148.0 B

Variable types

Numeric7
Text15

Alerts

Unnamed: 0 has unique valuesUnique
key_id has unique valuesUnique
home_team has 12718 (50.0%) zerosZeros
away_team has 12719 (50.0%) zerosZeros
starter has 4515 (17.7%) zerosZeros
substitute has 20922 (82.3%) zerosZeros

Reproduction

Analysis started2023-10-23 21:38:48.585120
Analysis finished2023-10-23 21:38:49.591310
Duration1.01 second
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct25437
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12718
Minimum0
Maximum25436
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-10-23T23:38:49.893453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1271.8
Q16359
median12718
Q319077
95-th percentile24164.2
Maximum25436
Range25436
Interquartile range (IQR)12718

Descriptive statistics

Standard deviation7343.173735
Coefficient of variation (CV)0.5773843163
Kurtosis-1.2
Mean12718
Median Absolute Deviation (MAD)6359
Skewness0
Sum323507766
Variance53922200.5
MonotonicityStrictly increasing
2023-10-23T23:38:50.240023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
16967 1
 
< 0.1%
16965 1
 
< 0.1%
16964 1
 
< 0.1%
16963 1
 
< 0.1%
16962 1
 
< 0.1%
16961 1
 
< 0.1%
16960 1
 
< 0.1%
16959 1
 
< 0.1%
16958 1
 
< 0.1%
Other values (25427) 25427
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
ValueCountFrequency (%)
25436 1
< 0.1%
25435 1
< 0.1%
25434 1
< 0.1%
25433 1
< 0.1%
25432 1
< 0.1%

key_id
Real number (ℝ)

UNIQUE 

Distinct25437
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12719
Minimum1
Maximum25437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-10-23T23:38:50.512795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1272.8
Q16360
median12719
Q319078
95-th percentile24165.2
Maximum25437
Range25436
Interquartile range (IQR)12718

Descriptive statistics

Standard deviation7343.173735
Coefficient of variation (CV)0.5773389209
Kurtosis-1.2
Mean12719
Median Absolute Deviation (MAD)6359
Skewness0
Sum323533203
Variance53922200.5
MonotonicityStrictly increasing
2023-10-23T23:38:50.754670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
16968 1
 
< 0.1%
16966 1
 
< 0.1%
16965 1
 
< 0.1%
16964 1
 
< 0.1%
16963 1
 
< 0.1%
16962 1
 
< 0.1%
16961 1
 
< 0.1%
16960 1
 
< 0.1%
16959 1
 
< 0.1%
Other values (25427) 25427
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
ValueCountFrequency (%)
25437 1
< 0.1%
25436 1
< 0.1%
25435 1
< 0.1%
25434 1
< 0.1%
25433 1
< 0.1%
Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:51.039652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters178059
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWC-1970
2nd rowWC-1970
3rd rowWC-1970
4th rowWC-1970
5th rowWC-1970
ValueCountFrequency (%)
wc-2018 1790
 
7.0%
wc-2014 1781
 
7.0%
wc-2006 1774
 
7.0%
wc-2010 1763
 
6.9%
wc-2002 1756
 
6.9%
wc-1998 1745
 
6.9%
wc-2019 1447
 
5.7%
wc-2015 1430
 
5.6%
wc-1994 1343
 
5.3%
wc-1990 1334
 
5.2%
Other values (10) 9274
36.5%
2023-10-23T23:38:51.449503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 25437
14.3%
C 25437
14.3%
- 25437
14.3%
0 23495
13.2%
1 21687
12.2%
9 18330
10.3%
2 17391
9.8%
8 7128
 
4.0%
4 4060
 
2.3%
7 3561
 
2.0%
Other values (3) 6096
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101748
57.1%
Uppercase Letter 50874
28.6%
Dash Punctuation 25437
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23495
23.1%
1 21687
21.3%
9 18330
18.0%
2 17391
17.1%
8 7128
 
7.0%
4 4060
 
4.0%
7 3561
 
3.5%
6 3107
 
3.1%
5 2121
 
2.1%
3 868
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
W 25437
50.0%
C 25437
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 25437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 127185
71.4%
Latin 50874
 
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 25437
20.0%
0 23495
18.5%
1 21687
17.1%
9 18330
14.4%
2 17391
13.7%
8 7128
 
5.6%
4 4060
 
3.2%
7 3561
 
2.8%
6 3107
 
2.4%
5 2121
 
1.7%
Latin
ValueCountFrequency (%)
W 25437
50.0%
C 25437
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178059
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 25437
14.3%
C 25437
14.3%
- 25437
14.3%
0 23495
13.2%
1 21687
12.2%
9 18330
10.3%
2 17391
9.8%
8 7128
 
4.0%
4 4060
 
2.3%
7 3561
 
2.0%
Other values (3) 6096
 
3.4%
Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:51.655656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length27
Median length25
Mean length25.535755
Min length25

Characters and Unicode

Total characters649553
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1970 FIFA Men's World Cup
2nd row1970 FIFA Men's World Cup
3rd row1970 FIFA Men's World Cup
4th row1970 FIFA Men's World Cup
5th row1970 FIFA Men's World Cup
ValueCountFrequency (%)
fifa 25437
20.0%
world 25437
20.0%
cup 25437
20.0%
men's 18623
14.6%
women's 6814
 
5.4%
2018 1790
 
1.4%
2014 1781
 
1.4%
2006 1774
 
1.4%
2010 1763
 
1.4%
2002 1756
 
1.4%
Other values (15) 16573
13.0%
2023-10-23T23:38:52.136792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
101748
 
15.7%
F 50874
 
7.8%
o 32251
 
5.0%
W 32251
 
5.0%
' 25437
 
3.9%
C 25437
 
3.9%
d 25437
 
3.9%
l 25437
 
3.9%
r 25437
 
3.9%
s 25437
 
3.9%
Other values (18) 279807
43.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 242561
37.3%
Uppercase Letter 178059
27.4%
Space Separator 101748
15.7%
Decimal Number 101748
15.7%
Other Punctuation 25437
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 32251
13.3%
d 25437
10.5%
l 25437
10.5%
r 25437
10.5%
s 25437
10.5%
n 25437
10.5%
p 25437
10.5%
e 25437
10.5%
u 25437
10.5%
m 6814
 
2.8%
Decimal Number
ValueCountFrequency (%)
0 23495
23.1%
1 21687
21.3%
9 18330
18.0%
2 17391
17.1%
8 7128
 
7.0%
4 4060
 
4.0%
7 3561
 
3.5%
6 3107
 
3.1%
5 2121
 
2.1%
3 868
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
F 50874
28.6%
W 32251
18.1%
C 25437
14.3%
A 25437
14.3%
I 25437
14.3%
M 18623
 
10.5%
Space Separator
ValueCountFrequency (%)
101748
100.0%
Other Punctuation
ValueCountFrequency (%)
' 25437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 420620
64.8%
Common 228933
35.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 50874
 
12.1%
o 32251
 
7.7%
W 32251
 
7.7%
C 25437
 
6.0%
d 25437
 
6.0%
l 25437
 
6.0%
r 25437
 
6.0%
s 25437
 
6.0%
n 25437
 
6.0%
p 25437
 
6.0%
Other values (6) 127185
30.2%
Common
ValueCountFrequency (%)
101748
44.4%
' 25437
 
11.1%
0 23495
 
10.3%
1 21687
 
9.5%
9 18330
 
8.0%
2 17391
 
7.6%
8 7128
 
3.1%
4 4060
 
1.8%
7 3561
 
1.6%
6 3107
 
1.4%
Other values (2) 2989
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
101748
 
15.7%
F 50874
 
7.8%
o 32251
 
5.0%
W 32251
 
5.0%
' 25437
 
3.9%
C 25437
 
3.9%
d 25437
 
3.9%
l 25437
 
3.9%
r 25437
 
3.9%
s 25437
 
3.9%
Other values (18) 279807
43.1%
Distinct951
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:52.463789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters228933
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM-1970-01
2nd rowM-1970-01
3rd rowM-1970-01
4th rowM-1970-01
5th rowM-1970-01
ValueCountFrequency (%)
m-2018-56 30
 
0.1%
m-2018-60 30
 
0.1%
m-2018-51 30
 
0.1%
m-2018-62 30
 
0.1%
m-2018-52 30
 
0.1%
m-2019-40 30
 
0.1%
m-2019-38 29
 
0.1%
m-2015-37 28
 
0.1%
m-2018-01 28
 
0.1%
m-2015-52 28
 
0.1%
Other values (941) 25144
98.8%
2023-10-23T23:38:53.058263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 50874
22.2%
0 30626
13.4%
1 29902
13.1%
M 25437
11.1%
2 25423
11.1%
9 20683
9.0%
4 9910
 
4.3%
8 9544
 
4.2%
3 7468
 
3.3%
5 6701
 
2.9%
Other values (2) 12365
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 152622
66.7%
Dash Punctuation 50874
 
22.2%
Uppercase Letter 25437
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30626
20.1%
1 29902
19.6%
2 25423
16.7%
9 20683
13.6%
4 9910
 
6.5%
8 9544
 
6.3%
3 7468
 
4.9%
5 6701
 
4.4%
6 6402
 
4.2%
7 5963
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 50874
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 25437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 203496
88.9%
Latin 25437
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 50874
25.0%
0 30626
15.0%
1 29902
14.7%
2 25423
12.5%
9 20683
10.2%
4 9910
 
4.9%
8 9544
 
4.7%
3 7468
 
3.7%
5 6701
 
3.3%
6 6402
 
3.1%
Latin
ValueCountFrequency (%)
M 25437
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 50874
22.2%
0 30626
13.4%
1 29902
13.1%
M 25437
11.1%
2 25423
11.1%
9 20683
9.0%
4 9910
 
4.3%
8 9544
 
4.2%
3 7468
 
3.3%
5 6701
 
2.9%
Other values (2) 12365
 
5.4%
Distinct748
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:53.330212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length36
Median length32
Mean length19.61351574
Min length12

Characters and Unicode

Total characters498909
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMexico vs Soviet Union
2nd rowMexico vs Soviet Union
3rd rowMexico vs Soviet Union
4th rowMexico vs Soviet Union
5th rowMexico vs Soviet Union
ValueCountFrequency (%)
vs 25437
29.8%
germany 3353
 
3.9%
brazil 2770
 
3.2%
england 2012
 
2.4%
argentina 1960
 
2.3%
united 1933
 
2.3%
italy 1893
 
2.2%
sweden 1879
 
2.2%
states 1856
 
2.2%
france 1841
 
2.2%
Other values (91) 40488
47.4%
2023-10-23T23:38:53.796079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
59985
 
12.0%
a 55572
 
11.1%
s 35122
 
7.0%
e 33564
 
6.7%
n 32727
 
6.6%
r 28902
 
5.8%
v 27499
 
5.5%
i 26342
 
5.3%
t 20064
 
4.0%
l 18871
 
3.8%
Other values (37) 160261
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 379528
76.1%
Space Separator 59985
 
12.0%
Uppercase Letter 59396
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 55572
14.6%
s 35122
9.3%
e 33564
8.8%
n 32727
 
8.6%
r 28902
 
7.6%
v 27499
 
7.2%
i 26342
 
6.9%
t 20064
 
5.3%
l 18871
 
5.0%
o 18662
 
4.9%
Other values (15) 82203
21.7%
Uppercase Letter
ValueCountFrequency (%)
S 9611
16.2%
C 5659
9.5%
N 4943
 
8.3%
A 4742
 
8.0%
B 4554
 
7.7%
G 4209
 
7.1%
U 3434
 
5.8%
I 3406
 
5.7%
E 2992
 
5.0%
P 2562
 
4.3%
Other values (11) 13284
22.4%
Space Separator
ValueCountFrequency (%)
59985
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 438924
88.0%
Common 59985
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 55572
 
12.7%
s 35122
 
8.0%
e 33564
 
7.6%
n 32727
 
7.5%
r 28902
 
6.6%
v 27499
 
6.3%
i 26342
 
6.0%
t 20064
 
4.6%
l 18871
 
4.3%
o 18662
 
4.3%
Other values (36) 141599
32.3%
Common
ValueCountFrequency (%)
59985
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
59985
 
12.0%
a 55572
 
11.1%
s 35122
 
7.0%
e 33564
 
6.7%
n 32727
 
6.6%
r 28902
 
5.8%
v 27499
 
5.5%
i 26342
 
5.3%
t 20064
 
4.0%
l 18871
 
3.8%
Other values (37) 160261
32.1%
Distinct380
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:54.151043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters254370
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1970-05-31
2nd row1970-05-31
3rd row1970-05-31
4th row1970-05-31
5th row1970-05-31
ValueCountFrequency (%)
1991-11-19 154
 
0.6%
1991-11-21 154
 
0.6%
1991-11-17 128
 
0.5%
2018-06-16 112
 
0.4%
2006-06-22 112
 
0.4%
2018-06-26 112
 
0.4%
2014-06-23 112
 
0.4%
2018-06-27 112
 
0.4%
2007-09-15 112
 
0.4%
2014-06-24 112
 
0.4%
Other values (370) 24217
95.2%
2023-10-23T23:38:54.711180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 56695
22.3%
- 50874
20.0%
1 36387
14.3%
2 28605
11.2%
6 25547
10.0%
9 21941
 
8.6%
8 9459
 
3.7%
7 9154
 
3.6%
4 6592
 
2.6%
5 4846
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 203496
80.0%
Dash Punctuation 50874
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56695
27.9%
1 36387
17.9%
2 28605
14.1%
6 25547
12.6%
9 21941
 
10.8%
8 9459
 
4.6%
7 9154
 
4.5%
4 6592
 
3.2%
5 4846
 
2.4%
3 4270
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 50874
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 254370
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56695
22.3%
- 50874
20.0%
1 36387
14.3%
2 28605
11.2%
6 25547
10.0%
9 21941
 
8.6%
8 9459
 
3.7%
7 9154
 
3.6%
4 6592
 
2.6%
5 4846
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 254370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56695
22.3%
- 50874
20.0%
1 36387
14.3%
2 28605
11.2%
6 25547
10.0%
9 21941
 
8.6%
8 9459
 
3.7%
7 9154
 
3.6%
4 6592
 
2.6%
5 4846
 
1.9%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:54.926560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length11
Mean length11.42776271
Min length5

Characters and Unicode

Total characters290688
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgroup stage
2nd rowgroup stage
3rd rowgroup stage
4th rowgroup stage
5th rowgroup stage
ValueCountFrequency (%)
group 19254
37.8%
stage 19254
37.8%
round 2379
 
4.7%
of 2379
 
4.7%
16 2379
 
4.7%
quarter-finals 1067
 
2.1%
second 902
 
1.8%
quarter-final 757
 
1.5%
semi-finals 584
 
1.1%
third-place 534
 
1.0%
Other values (3) 1396
 
2.7%
2023-10-23T23:38:55.395589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 38508
13.2%
r 25815
8.9%
25448
8.8%
a 25416
8.7%
o 24914
8.6%
e 23462
8.1%
u 23457
8.1%
s 22755
7.8%
t 22146
7.6%
p 19788
6.8%
Other values (12) 38979
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 257176
88.5%
Space Separator 25448
 
8.8%
Decimal Number 4758
 
1.6%
Dash Punctuation 3306
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 38508
15.0%
r 25815
10.0%
a 25416
9.9%
o 24914
9.7%
e 23462
9.1%
u 23457
9.1%
s 22755
8.8%
t 22146
8.6%
p 19788
7.7%
n 6551
 
2.5%
Other values (8) 24364
9.5%
Decimal Number
ValueCountFrequency (%)
6 2379
50.0%
1 2379
50.0%
Space Separator
ValueCountFrequency (%)
25448
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 257176
88.5%
Common 33512
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 38508
15.0%
r 25815
10.0%
a 25416
9.9%
o 24914
9.7%
e 23462
9.1%
u 23457
9.1%
s 22755
8.8%
t 22146
8.6%
p 19788
7.7%
n 6551
 
2.5%
Other values (8) 24364
9.5%
Common
ValueCountFrequency (%)
25448
75.9%
- 3306
 
9.9%
6 2379
 
7.1%
1 2379
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 290688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 38508
13.2%
r 25815
8.9%
25448
8.8%
a 25416
8.7%
o 24914
8.6%
e 23462
8.1%
u 23457
8.1%
s 22755
7.8%
t 22146
7.6%
p 19788
6.8%
Other values (12) 38979
13.4%
Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:55.598114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length14
Median length7
Mean length8.701497818
Min length7

Characters and Unicode

Total characters221340
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGroup 1
2nd rowGroup 1
3rd rowGroup 1
4th rowGroup 1
5th rowGroup 1
ValueCountFrequency (%)
group 19254
37.8%
not 6183
 
12.2%
applicable 6183
 
12.2%
b 2901
 
5.7%
a 2886
 
5.7%
c 2609
 
5.1%
d 2284
 
4.5%
e 1797
 
3.5%
f 1786
 
3.5%
h 1000
 
2.0%
Other values (7) 3991
 
7.8%
2023-10-23T23:38:56.032021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 31620
14.3%
o 25437
11.5%
25437
11.5%
G 20244
9.1%
u 19254
8.7%
r 19254
8.7%
a 12366
 
5.6%
l 12366
 
5.6%
c 6211
 
2.8%
e 6183
 
2.8%
Other values (17) 42968
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 157423
71.1%
Uppercase Letter 35479
 
16.0%
Space Separator 25437
 
11.5%
Decimal Number 3001
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 31620
20.1%
o 25437
16.2%
u 19254
12.2%
r 19254
12.2%
a 12366
 
7.9%
l 12366
 
7.9%
c 6211
 
3.9%
e 6183
 
3.9%
n 6183
 
3.9%
b 6183
 
3.9%
Other values (2) 12366
 
7.9%
Uppercase Letter
ValueCountFrequency (%)
G 20244
57.1%
B 2901
 
8.2%
A 2886
 
8.1%
C 2581
 
7.3%
D 2284
 
6.4%
E 1797
 
5.1%
F 1786
 
5.0%
H 1000
 
2.8%
Decimal Number
ValueCountFrequency (%)
4 682
22.7%
3 681
22.7%
1 675
22.5%
2 663
22.1%
6 151
 
5.0%
5 149
 
5.0%
Space Separator
ValueCountFrequency (%)
25437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 192902
87.2%
Common 28438
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 31620
16.4%
o 25437
13.2%
G 20244
10.5%
u 19254
10.0%
r 19254
10.0%
a 12366
 
6.4%
l 12366
 
6.4%
c 6211
 
3.2%
e 6183
 
3.2%
n 6183
 
3.2%
Other values (10) 33784
17.5%
Common
ValueCountFrequency (%)
25437
89.4%
4 682
 
2.4%
3 681
 
2.4%
1 675
 
2.4%
2 663
 
2.3%
6 151
 
0.5%
5 149
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 31620
14.3%
o 25437
11.5%
25437
11.5%
G 20244
9.1%
u 19254
8.7%
r 19254
8.7%
a 12366
 
5.6%
l 12366
 
5.6%
c 6211
 
2.8%
e 6183
 
2.8%
Other values (17) 42968
19.4%
Distinct84
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:56.352977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters101748
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT-46
2nd rowT-46
3rd rowT-46
4th rowT-46
5th rowT-46
ValueCountFrequency (%)
t-09 1375
 
5.4%
t-31 1103
 
4.3%
t-28 1010
 
4.0%
t-03 984
 
3.9%
t-41 948
 
3.7%
t-74 944
 
3.7%
t-83 934
 
3.7%
t-30 923
 
3.6%
t-48 780
 
3.1%
t-73 746
 
2.9%
Other values (74) 15690
61.7%
2023-10-23T23:38:56.883151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 25437
25.0%
- 25437
25.0%
4 7306
 
7.2%
3 6904
 
6.8%
1 6546
 
6.4%
0 6226
 
6.1%
8 5200
 
5.1%
7 4715
 
4.6%
6 4067
 
4.0%
5 3979
 
3.9%
Other values (2) 5931
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50874
50.0%
Uppercase Letter 25437
25.0%
Dash Punctuation 25437
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7306
14.4%
3 6904
13.6%
1 6546
12.9%
0 6226
12.2%
8 5200
10.2%
7 4715
9.3%
6 4067
8.0%
5 3979
7.8%
2 3907
7.7%
9 2024
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
T 25437
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76311
75.0%
Latin 25437
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 25437
33.3%
4 7306
 
9.6%
3 6904
 
9.0%
1 6546
 
8.6%
0 6226
 
8.2%
8 5200
 
6.8%
7 4715
 
6.2%
6 4067
 
5.3%
5 3979
 
5.2%
2 3907
 
5.1%
Latin
ValueCountFrequency (%)
T 25437
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 25437
25.0%
- 25437
25.0%
4 7306
 
7.2%
3 6904
 
6.8%
1 6546
 
6.4%
0 6226
 
6.1%
8 5200
 
5.1%
7 4715
 
4.6%
6 4067
 
4.0%
5 3979
 
3.9%
Other values (2) 5931
 
5.8%
Distinct84
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:57.269483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.809254236
Min length4

Characters and Unicode

Total characters198644
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMexico
2nd rowMexico
3rd rowMexico
4th rowMexico
5th rowMexico
ValueCountFrequency (%)
germany 1679
 
5.6%
brazil 1375
 
4.6%
england 1010
 
3.4%
argentina 984
 
3.3%
united 973
 
3.2%
italy 948
 
3.2%
sweden 944
 
3.1%
states 934
 
3.1%
france 923
 
3.1%
netherlands 780
 
2.6%
Other values (90) 19448
64.8%
2023-10-23T23:38:58.002961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 27779
14.0%
e 16798
 
8.5%
n 16387
 
8.2%
r 14443
 
7.3%
i 13162
 
6.6%
t 10056
 
5.1%
l 9429
 
4.7%
o 9328
 
4.7%
d 6559
 
3.3%
u 5810
 
2.9%
Other values (37) 68893
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 164379
82.8%
Uppercase Letter 29704
 
15.0%
Space Separator 4561
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 27779
16.9%
e 16798
10.2%
n 16387
10.0%
r 14443
8.8%
i 13162
 
8.0%
t 10056
 
6.1%
l 9429
 
5.7%
o 9328
 
5.7%
d 6559
 
4.0%
u 5810
 
3.5%
Other values (15) 34628
21.1%
Uppercase Letter
ValueCountFrequency (%)
S 4815
16.2%
C 2823
9.5%
N 2467
 
8.3%
A 2371
 
8.0%
B 2266
 
7.6%
G 2105
 
7.1%
U 1729
 
5.8%
I 1703
 
5.7%
E 1502
 
5.1%
P 1284
 
4.3%
Other values (11) 6639
22.4%
Space Separator
ValueCountFrequency (%)
4561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194083
97.7%
Common 4561
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 27779
14.3%
e 16798
 
8.7%
n 16387
 
8.4%
r 14443
 
7.4%
i 13162
 
6.8%
t 10056
 
5.2%
l 9429
 
4.9%
o 9328
 
4.8%
d 6559
 
3.4%
u 5810
 
3.0%
Other values (36) 64332
33.1%
Common
ValueCountFrequency (%)
4561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 27779
14.0%
e 16798
 
8.5%
n 16387
 
8.2%
r 14443
 
7.3%
i 13162
 
6.6%
t 10056
 
5.1%
l 9429
 
4.7%
o 9328
 
4.7%
d 6559
 
3.3%
u 5810
 
2.9%
Other values (37) 68893
34.7%
Distinct83
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:38:58.349682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters76311
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEX
2nd rowMEX
3rd rowMEX
4th rowMEX
5th rowMEX
ValueCountFrequency (%)
deu 1603
 
6.3%
bra 1375
 
5.4%
eng 1010
 
4.0%
arg 984
 
3.9%
ita 948
 
3.7%
swe 944
 
3.7%
usa 934
 
3.7%
fra 923
 
3.6%
nld 780
 
3.1%
esp 746
 
2.9%
Other values (73) 15190
59.7%
2023-10-23T23:38:58.936432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 8872
 
11.6%
A 8161
 
10.7%
E 6435
 
8.4%
N 6344
 
8.3%
U 5540
 
7.3%
S 4761
 
6.2%
G 3692
 
4.8%
C 3436
 
4.5%
D 3293
 
4.3%
L 3004
 
3.9%
Other values (16) 22773
29.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 76311
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 8872
 
11.6%
A 8161
 
10.7%
E 6435
 
8.4%
N 6344
 
8.3%
U 5540
 
7.3%
S 4761
 
6.2%
G 3692
 
4.8%
C 3436
 
4.5%
D 3293
 
4.3%
L 3004
 
3.9%
Other values (16) 22773
29.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 76311
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 8872
 
11.6%
A 8161
 
10.7%
E 6435
 
8.4%
N 6344
 
8.3%
U 5540
 
7.3%
S 4761
 
6.2%
G 3692
 
4.8%
C 3436
 
4.5%
D 3293
 
4.3%
L 3004
 
3.9%
Other values (16) 22773
29.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 8872
 
11.6%
A 8161
 
10.7%
E 6435
 
8.4%
N 6344
 
8.3%
U 5540
 
7.3%
S 4761
 
6.2%
G 3692
 
4.8%
C 3436
 
4.5%
D 3293
 
4.3%
L 3004
 
3.9%
Other values (16) 22773
29.8%

home_team
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5000196564
Minimum0
Maximum1
Zeros12718
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-10-23T23:38:59.157446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5000098281
Coefficient of variation (CV)0.9999803442
Kurtosis-2.000157264
Mean0.5000196564
Median Absolute Deviation (MAD)0
Skewness-7.863026099 × 10-5
Sum12719
Variance0.2500098282
MonotonicityNot monotonic
2023-10-23T23:38:59.353219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 12719
50.0%
0 12718
50.0%
ValueCountFrequency (%)
0 12718
50.0%
1 12719
50.0%
ValueCountFrequency (%)
1 12719
50.0%
0 12718
50.0%

away_team
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4999803436
Minimum0
Maximum1
Zeros12719
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-10-23T23:38:59.550621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5000098281
Coefficient of variation (CV)1.000058971
Kurtosis-2.000157264
Mean0.4999803436
Median Absolute Deviation (MAD)0
Skewness7.863026099 × 10-5
Sum12718
Variance0.2500098282
MonotonicityNot monotonic
2023-10-23T23:38:59.757477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 12719
50.0%
1 12718
50.0%
ValueCountFrequency (%)
0 12719
50.0%
1 12718
50.0%
ValueCountFrequency (%)
1 12718
50.0%
0 12719
50.0%
Distinct6086
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:39:00.134267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters178059
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique992 ?
Unique (%)3.9%

Sample

1st rowP-66980
2nd rowP-64553
3rd rowP-42664
4th rowP-69898
5th rowP-56971
ValueCountFrequency (%)
p-49502 25
 
0.1%
p-62104 24
 
0.1%
p-25850 24
 
0.1%
p-89236 24
 
0.1%
p-27787 24
 
0.1%
p-43222 23
 
0.1%
p-80404 21
 
0.1%
p-19610 21
 
0.1%
p-97813 21
 
0.1%
p-41187 21
 
0.1%
Other values (6076) 25209
99.1%
2023-10-23T23:39:00.883097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 25437
14.3%
- 25437
14.3%
9 13343
7.5%
8 12930
7.3%
4 12888
7.2%
2 12885
7.2%
7 12868
7.2%
3 12781
7.2%
6 12761
7.2%
5 12382
7.0%
Other values (2) 24347
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 127185
71.4%
Uppercase Letter 25437
 
14.3%
Dash Punctuation 25437
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 13343
10.5%
8 12930
10.2%
4 12888
10.1%
2 12885
10.1%
7 12868
10.1%
3 12781
10.0%
6 12761
10.0%
5 12382
9.7%
0 12277
9.7%
1 12070
9.5%
Uppercase Letter
ValueCountFrequency (%)
P 25437
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 152622
85.7%
Latin 25437
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 25437
16.7%
9 13343
8.7%
8 12930
8.5%
4 12888
8.4%
2 12885
8.4%
7 12868
8.4%
3 12781
8.4%
6 12761
8.4%
5 12382
8.1%
0 12277
8.0%
Latin
ValueCountFrequency (%)
P 25437
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178059
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 25437
14.3%
- 25437
14.3%
9 13343
7.5%
8 12930
7.3%
4 12888
7.2%
2 12885
7.2%
7 12868
7.2%
3 12781
7.2%
6 12761
7.2%
5 12382
7.0%
Other values (2) 24347
13.7%
Distinct5068
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:39:01.215671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length23
Median length20
Mean length6.760506349
Min length1

Characters and Unicode

Total characters171967
Distinct characters118
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique745 ?
Unique (%)2.9%

Sample

1st rowCalderón
2nd rowPeña
3rd rowPérez
4th rowHernández
5th rowSalgado
ValueCountFrequency (%)
van 248
 
0.9%
de 210
 
0.8%
kim 133
 
0.5%
lee 111
 
0.4%
rodríguez 78
 
0.3%
silva 68
 
0.3%
müller 62
 
0.2%
der 57
 
0.2%
larsson 50
 
0.2%
sánchez 48
 
0.2%
Other values (5074) 25313
96.0%
2023-10-23T23:39:01.842957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 17630
 
10.3%
e 15570
 
9.1%
i 12183
 
7.1%
o 11778
 
6.8%
n 11471
 
6.7%
r 11448
 
6.7%
l 8062
 
4.7%
s 7492
 
4.4%
t 5442
 
3.2%
u 5130
 
3.0%
Other values (108) 65761
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 144143
83.8%
Uppercase Letter 26473
 
15.4%
Space Separator 941
 
0.5%
Dash Punctuation 272
 
0.2%
Other Punctuation 138
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17630
12.2%
e 15570
10.8%
i 12183
 
8.5%
o 11778
 
8.2%
n 11471
 
8.0%
r 11448
 
7.9%
l 8062
 
5.6%
s 7492
 
5.2%
t 5442
 
3.8%
u 5130
 
3.6%
Other values (63) 37937
26.3%
Uppercase Letter
ValueCountFrequency (%)
S 2522
 
9.5%
M 2348
 
8.9%
B 2236
 
8.4%
C 1647
 
6.2%
A 1527
 
5.8%
K 1526
 
5.8%
L 1301
 
4.9%
R 1301
 
4.9%
G 1274
 
4.8%
P 1244
 
4.7%
Other values (32) 9547
36.1%
Space Separator
ValueCountFrequency (%)
941
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 272
100.0%
Other Punctuation
ValueCountFrequency (%)
' 138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 170616
99.2%
Common 1351
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17630
 
10.3%
e 15570
 
9.1%
i 12183
 
7.1%
o 11778
 
6.9%
n 11471
 
6.7%
r 11448
 
6.7%
l 8062
 
4.7%
s 7492
 
4.4%
t 5442
 
3.2%
u 5130
 
3.0%
Other values (105) 64410
37.8%
Common
ValueCountFrequency (%)
941
69.7%
- 272
 
20.1%
' 138
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167776
97.6%
None 4191
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 17630
 
10.5%
e 15570
 
9.3%
i 12183
 
7.3%
o 11778
 
7.0%
n 11471
 
6.8%
r 11448
 
6.8%
l 8062
 
4.8%
s 7492
 
4.5%
t 5442
 
3.2%
u 5130
 
3.1%
Other values (45) 61570
36.7%
None
ValueCountFrequency (%)
ć 572
13.6%
é 553
13.2%
á 483
11.5%
í 457
 
10.9%
ö 234
 
5.6%
ó 209
 
5.0%
ü 151
 
3.6%
ñ 134
 
3.2%
ø 122
 
2.9%
č 106
 
2.5%
Other values (53) 1170
27.9%
Distinct3086
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:39:02.246634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length19
Median length16
Mean length6.474662893
Min length2

Characters and Unicode

Total characters164696
Distinct characters101
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique350 ?
Unique (%)1.4%

Sample

1st rowIgnacio
2nd rowGustavo
3rd rowMario
4th rowGuillermo
5th rowHoracio López
ValueCountFrequency (%)
not 1430
 
5.2%
applicable 1430
 
5.2%
carlos 213
 
0.8%
luis 182
 
0.7%
josé 179
 
0.7%
david 152
 
0.6%
roberto 150
 
0.5%
thomas 142
 
0.5%
john 131
 
0.5%
fernando 125
 
0.5%
Other values (3045) 23239
84.9%
2023-10-23T23:39:02.862120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 18360
 
11.1%
i 14289
 
8.7%
e 13594
 
8.3%
n 12962
 
7.9%
o 10987
 
6.7%
l 9835
 
6.0%
r 9732
 
5.9%
t 5561
 
3.4%
s 5187
 
3.1%
u 4134
 
2.5%
Other values (91) 60055
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 137021
83.2%
Uppercase Letter 24742
 
15.0%
Space Separator 1936
 
1.2%
Dash Punctuation 984
 
0.6%
Other Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18360
13.4%
i 14289
10.4%
e 13594
9.9%
n 12962
9.5%
o 10987
 
8.0%
l 9835
 
7.2%
r 9732
 
7.1%
t 5561
 
4.1%
s 5187
 
3.8%
u 4134
 
3.0%
Other values (47) 32380
23.6%
Uppercase Letter
ValueCountFrequency (%)
M 2411
 
9.7%
A 2278
 
9.2%
J 2273
 
9.2%
S 1719
 
6.9%
R 1505
 
6.1%
C 1397
 
5.6%
D 1237
 
5.0%
L 1205
 
4.9%
G 1096
 
4.4%
K 1014
 
4.1%
Other values (31) 8607
34.8%
Space Separator
ValueCountFrequency (%)
1936
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 984
100.0%
Other Punctuation
ValueCountFrequency (%)
' 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 161763
98.2%
Common 2933
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18360
 
11.3%
i 14289
 
8.8%
e 13594
 
8.4%
n 12962
 
8.0%
o 10987
 
6.8%
l 9835
 
6.1%
r 9732
 
6.0%
t 5561
 
3.4%
s 5187
 
3.2%
u 4134
 
2.6%
Other values (88) 57122
35.3%
Common
ValueCountFrequency (%)
1936
66.0%
- 984
33.5%
' 13
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162401
98.6%
None 2295
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18360
 
11.3%
i 14289
 
8.8%
e 13594
 
8.4%
n 12962
 
8.0%
o 10987
 
6.8%
l 9835
 
6.1%
r 9732
 
6.0%
t 5561
 
3.4%
s 5187
 
3.2%
u 4134
 
2.5%
Other values (45) 57760
35.6%
None
ValueCountFrequency (%)
é 659
28.7%
á 293
12.8%
í 221
 
9.6%
ó 187
 
8.1%
ł 99
 
4.3%
ü 92
 
4.0%
ú 89
 
3.9%
ë 77
 
3.4%
Á 71
 
3.1%
š 69
 
3.0%
Other values (36) 438
19.1%

shirt_number
Real number (ℝ)

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.29284114
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-10-23T23:39:03.080773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile21
Maximum23
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.133736605
Coefficient of variation (CV)0.5959225954
Kurtosis-1.042409125
Mean10.29284114
Median Absolute Deviation (MAD)5
Skewness0.2655827522
Sum261819
Variance37.62272474
MonotonicityNot monotonic
2023-10-23T23:39:03.285913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10 1544
 
6.1%
9 1479
 
5.8%
1 1444
 
5.7%
11 1430
 
5.6%
6 1415
 
5.6%
8 1412
 
5.6%
7 1395
 
5.5%
4 1383
 
5.4%
5 1374
 
5.4%
3 1370
 
5.4%
Other values (13) 11191
44.0%
ValueCountFrequency (%)
1 1444
5.7%
2 1303
5.1%
3 1370
5.4%
4 1383
5.4%
5 1374
5.4%
ValueCountFrequency (%)
23 277
 
1.1%
22 460
1.8%
21 690
2.7%
20 944
3.7%
19 913
3.6%
Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:39:03.495036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length20
Median length16
Mean length10.83810984
Min length7

Characters and Unicode

Total characters275689
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgoal keeper
2nd rowdefender
3rd rowdefender
4th rowmidfielder
5th rowforward
ValueCountFrequency (%)
midfielder 8809
22.9%
center 6018
15.6%
forward 5515
14.3%
back 4369
11.3%
defender 3722
9.7%
left 2085
 
5.4%
right 2083
 
5.4%
goal 1937
 
5.0%
keeper 1937
 
5.0%
winger 859
 
2.2%
Other values (6) 1190
 
3.1%
2023-10-23T23:39:04.005938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 50975
18.5%
r 34769
12.6%
d 30931
11.2%
i 21524
 
7.8%
f 20400
 
7.4%
13087
 
4.7%
l 12831
 
4.7%
a 12747
 
4.6%
n 11563
 
4.2%
t 11197
 
4.1%
Other values (11) 55665
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 262602
95.3%
Space Separator 13087
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 50975
19.4%
r 34769
13.2%
d 30931
11.8%
i 21524
8.2%
f 20400
7.8%
l 12831
 
4.9%
a 12747
 
4.9%
n 11563
 
4.4%
t 11197
 
4.3%
c 10935
 
4.2%
Other values (10) 44730
17.0%
Space Separator
ValueCountFrequency (%)
13087
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 262602
95.3%
Common 13087
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 50975
19.4%
r 34769
13.2%
d 30931
11.8%
i 21524
8.2%
f 20400
7.8%
l 12831
 
4.9%
a 12747
 
4.9%
n 11563
 
4.4%
t 11197
 
4.3%
c 10935
 
4.2%
Other values (10) 44730
17.0%
Common
ValueCountFrequency (%)
13087
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 275689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 50975
18.5%
r 34769
12.6%
d 30931
11.2%
i 21524
 
7.8%
f 20400
 
7.4%
13087
 
4.7%
l 12831
 
4.7%
a 12747
 
4.6%
n 11563
 
4.2%
t 11197
 
4.1%
Other values (11) 55665
20.2%
Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size198.9 KiB
2023-10-23T23:39:04.227262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.005778983
Min length2

Characters and Unicode

Total characters51021
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGK
2nd rowDF
3rd rowDF
4th rowMF
5th rowFW
ValueCountFrequency (%)
mf 4937
19.4%
df 3722
14.6%
fw 3697
14.5%
cb 2373
9.3%
cm 2137
8.4%
gk 1937
 
7.6%
cf 1508
 
5.9%
rb 927
 
3.6%
lb 922
 
3.6%
rm 502
 
2.0%
Other values (11) 2775
10.9%
2023-10-23T23:39:04.632501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 14174
27.8%
M 8809
17.3%
C 6018
11.8%
W 4844
 
9.5%
B 4369
 
8.6%
D 3991
 
7.8%
L 2085
 
4.1%
R 2083
 
4.1%
G 1937
 
3.8%
K 1937
 
3.8%
Other values (2) 774
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 51021
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 14174
27.8%
M 8809
17.3%
C 6018
11.8%
W 4844
 
9.5%
B 4369
 
8.6%
D 3991
 
7.8%
L 2085
 
4.1%
R 2083
 
4.1%
G 1937
 
3.8%
K 1937
 
3.8%
Other values (2) 774
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 51021
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 14174
27.8%
M 8809
17.3%
C 6018
11.8%
W 4844
 
9.5%
B 4369
 
8.6%
D 3991
 
7.8%
L 2085
 
4.1%
R 2083
 
4.1%
G 1937
 
3.8%
K 1937
 
3.8%
Other values (2) 774
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 14174
27.8%
M 8809
17.3%
C 6018
11.8%
W 4844
 
9.5%
B 4369
 
8.6%
D 3991
 
7.8%
L 2085
 
4.1%
R 2083
 
4.1%
G 1937
 
3.8%
K 1937
 
3.8%
Other values (2) 774
 
1.5%

starter
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8225026536
Minimum0
Maximum1
Zeros4515
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-10-23T23:39:04.817080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3820965559
Coefficient of variation (CV)0.4645535843
Kurtosis0.8500915476
Mean0.8225026536
Median Absolute Deviation (MAD)0
Skewness-1.688201622
Sum20922
Variance0.145997778
MonotonicityNot monotonic
2023-10-23T23:39:04.991947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 20922
82.3%
0 4515
 
17.7%
ValueCountFrequency (%)
0 4515
 
17.7%
1 20922
82.3%
ValueCountFrequency (%)
1 20922
82.3%
0 4515
 
17.7%

substitute
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1774973464
Minimum0
Maximum1
Zeros20922
Zeros (%)82.3%
Negative0
Negative (%)0.0%
Memory size99.5 KiB
2023-10-23T23:39:05.144079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3820965559
Coefficient of variation (CV)2.152688835
Kurtosis0.8500915476
Mean0.1774973464
Median Absolute Deviation (MAD)0
Skewness1.688201622
Sum4515
Variance0.145997778
MonotonicityNot monotonic
2023-10-23T23:39:05.307480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 20922
82.3%
1 4515
 
17.7%
ValueCountFrequency (%)
0 20922
82.3%
1 4515
 
17.7%
ValueCountFrequency (%)
1 4515
 
17.7%
0 20922
82.3%